Method and apparatus for topology aggregation and routing controller

ABSTRACT

The present invention provides a method and an apparatus for topology aggregation and a routing controller. The method comprises: obtaining a link-diversity path number c(i,j,λk) between each pair of border nodes in a real topology GR; and obtaining a connectivity matrix C by utilizing said link-diversity path number c(i,j,λk) and constructing a corresponding full-mesh topology GB. Moreover, the method further comprises compressing the resulting full-mesh topology into a bi-directional shuffle-net topology using the genetic algorithm. Therefore, according the present invention, more link information can be provided for routing decision and more accurate topology can be provided.

FIELD OF THE INVENTION

The present invention relates to the network technology, and moreparticularly, to a method and an apparatus for topology aggregation anda routing controller.

BACKGROUND OF THE INVENTION

Multiple routing domains are unexceptionally included in the IP network,the ATM (Asynchronous Transmission Mode) network or the emerging ASON

(Automatic Switched Optical Network).

For the sake of scalability and security, internal topology informationof each routing domain can be advertised to other routing domains in thenetwork only after being aggregated by means of some topologyaggregation method. Thus, each routing domain only maintains its owndetailed topology information as well as aggregated topology informationof other routing domains, thereby reducing the amount of informationneeding to be advertised and stored in the network.

Generally, a topology aggregation process includes firstly constructinga full-mesh topology composed of border nodes based on a real topology,and further compressing, which is optional, the full-mesh topology intoa sparser topology, such as a star topology, a tree topology and thelike.

FIG. 1 shows an example of a topology aggregation process. As shown inFIG. 1, topology 100 is a real topology comprising eight nodes 1-8, tenlinks, and two working wavelengths λ₁ and λ₂. In FIG. 1, the Nodes 1-4are border nodes connected to external peer domains, the Nodes 5-8 areinternal nodes, the solid line/the dashed line between nodes indicatesthe wavelength λ₁/λ₂ path of the link is free and a new connection canbe established.

During the topology aggregation, internal nodes of topology 100 areconcealed, and only border nodes 1-4 and resource availabilitytherebetween are reserved. The connectivity relation among these fourborder nodes can be represented by a connectivity matrix as shown informula (1).

$\begin{matrix}{{{{Matrix}\left( \lambda_{1} \right)} = \begin{bmatrix} - & 0 & 1 & 1 \\0 & - & 0 & 0 \\1 & 0 & - & 1 \\1 & 0 & 1 & - \end{bmatrix}},\mspace{14mu}{{{Matrix}\left( \lambda_{2} \right)} = \begin{bmatrix} - & 1 & 1 & 0 \\1 & - & 1 & 0 \\1 & 1 & - & 0 \\0 & 0 & 0 & - \end{bmatrix}}} & {{formula}\mspace{14mu}(1)}\end{matrix}$

Topology 101 shown in FIG. 1 has 4 border nodes 1-4, and thus theconnectivity among border nodes can be represented by a 4×4 matrix foreach wavelength λ₁ and λ₂. If there is a logical link enabling twoborder nodes to be connected (for example, node 1 and node 4 can beconnected with each other through wavelength λ₁ path 1-5-6-3-4), thecorresponding element of the matrix is set to 1, and 0 otherwise.

Topology 101, which includes only border nodes 1-4, is a full-meshtopology constructed in accordance with the connectivity matrix. It canbe seen that the number of links is reduced to 5 after the full-meshtopology construction (in the worst case, the number of links is 6,i.e., all nodes are connected).

Topology 102 is the resultant topology after further compressing thetopology 101. The redundant logical links in the former topology 101 aredeleted, for example, the logical link of wavelength λ₁ between node 3and node 4 is replaced with the path 3-1-4, and the logical link ofwavelength λ₂ between node 2 and node 3 is replaced with the path 2-1-3.After compressing, the total number of links is further reduced to 3.

The existing topology aggregation technology is designed for the ATMnetwork. There are three familiar methods, including the symmetric-nodeapproach, the full-mesh approach and the star approach. The main idea ofthe symmetric-node approach is that all border nodes of the realtopology are merged into a single virtual node and the connectionproperty between border nodes is represented by a certain common value.The advantage of this approach is that only a very little amount ofinformation needs to be exchanged. However, the disadvantage is that theprovided information is too rough and inaccurate excessively, which willcause that the intra-domain resources can't be utilized appropriately.The full-mesh approach aims at the accuracy of the aggregationinformation. It assumes that all border nodes of the real topology arefull connected by logical links, each of which is configured with one ormore QoS parameters, such as delay or bandwidth. This approach reservesthe connectivity property between border nodes of the original realtopology accurately. However, because it must maintain the informationof N(N-1)/2(N is the number of border nodes) logical links, when thenetwork size is relatively large, the scalability is poor. The starapproach assumes that there is a virtual node in the center, and all theborder nodes in real topology are connected to it by logical links.Furthermore, each logical link may have different property. Thus, thestar approach can represent more detailed link information, and may bemuch more accurate than the symmetric-node approach. At the same time,the star approach only needs to maintain the information of N logicallinks, and thus has better scalability than the full-mesh approach andis suitable for a relatively larger network.

However, when the above-mentioned topology aggregation methods designedfor the ATM network are applied to the optical network, these methodswill be too rough and inaccurate to reach a good performance as acondition of wavelength continuity constraint needs to be met.Therefore, a more suitable solution for topology aggregation is requiredfor the optical network such as ASON.

SUMMARY OF THE INVENTION

To this end, it is an object of the present invention to provide asolution for topology aggregation more suitable for the optical network.

According to an aspect of the present invention, there is provided amethod for topology aggregation, comprising:

obtaining the link-diversity path number c(i,j,λ_(k)) between each pairof border nodes in a real topology G_(R); and

obtaining a connectivity matrix C by utilizing said link-diversity pathnumber c(i,j,λ_(k)) and constructing a corresponding full-mesh topologyG_(B).

According to another aspect of the present invention, there is providedan apparatus for topology aggregation, comprising:

link-diversity path number obtaining means configured to obtain thelink-diversity path number c(i,j,λ_(k)) between each pair of bordernodes in a real topology G_(R);

topology constructing means configured to obtain connectivity matrix Cby using said link-diversity path number c(i,j,λ_(k)) and construct acorresponding full-mesh topology G_(B).

According to another aspect of the present invention, there is provideda routing controller, comprising: the apparatus for topology aggregationas described above; and

link-state sending means configured to generate the Link-StateAdvertisement LSA information based on the topology obtained by theapparatus for topology aggregation, and send said LSA information out.

More link information for routing decision and more accurate topologycan be provided according the present invention.

BRIEF DESCRIPTION ON THE DRAWINGS

Other objects and effects of the present invention will be more apparentand easy to understand upon the comprehensive understanding of thepresent invention from the following detailed description, taken withreference to the drawings, wherein:

FIG. 1 shows an example of a topology aggregation process according tothe prior art;

FIG. 2 shows a flow chart of a method for topology aggregation accordingto an embodiment of the present invention;

FIG. 3 shows a flow chart of a topology compression process according toan embodiment of the present invention;

FIG. 4 shows an example of a bi-directional shuffle-net topology;

FIG. 5 shows a flow chart of a process of aggregating into abi-directional shuffle-net topology according to an embodiment of thepresent invention;

FIG. 6 shows a flow chart of a process of optimizing a bi-directionalshuffle-net topology according to an embodiment of the presentinvention;

FIG. 7 shows how to map nodes in a full-mesh topology to abi-directional shuffle-net;

FIG. 8 shows an example of crossover and mutation operations accordingto an embodiment of the present invention;

FIG. 9 shows a schematic block diagram of an apparatus for topologyaggregation according to an embodiment of the present invention;

FIG. 10 shows a schematic block diagram of an apparatus for topologyaggregation according to another embodiment of the present invention;

FIG. 11 shows a schematic block diagram of bi-directional shuffle-nettopology optimizing means in an apparatus for topology aggregationaccording to another embodiment of the present invention;

FIG. 12 shows a schematic block diagram of an apparatus for topologyaggregation according to another embodiment of the present invention;

FIG. 13 shows a schematic block diagram of a routing controlleraccording to an embodiment of the present invention;

FIG. 14 shows a starting phase flowchart of a routing controlleraccording to an embodiment of the present invention; and

FIG. 15 shows a working phase flowchart of a routing controlleraccording to an embodiment of the present invention;

The Like reference number indicates the same, similar, or correspondingfeatures or functions throughout the drawings mentioned above.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, the present invention will be described in detail by meansof specific embodiments with reference to the drawings.

FIG. 2 shows a flow chart of a method for topology aggregation accordingto an embodiment of the present invention. In the following descriptionof embodiments of the present invention, the topology aggregation isdirected to one domain in AOSN.

As shown in FIG. 2, firstly, a real topology of the domain is obtainedat step 210.G _(R)=(V, E)   formula (2)

Wherein, V represents the set of nodes in the topology, i.e., V={v₁, v₂,. . . , v_(n)}, and E represents the set of links among nodes, i. e.,E={e₁, e₂, . . . , e_(m)} and wherein each link can support one or moreof wavelengths λ₁, λ₂, . . . , λ_(W), n is the total number of nodes,and m is the total number of links, and W is the total number ofwavelengths. It should be noted that G_(R) can be a multi-graph whereinthere are multiple optical fiber links between two nodes.

Next, at step 220, for each of wavelengths λ₁, λ₂, . . . , λ_(W), anadjacent matrix which involves all nodes is obtained:R _(λ) _(k) =[r(i,j,λ _(k))]_(n×x)   formula (3)wherein (i, j=1, . . . , n, k=1, . . . , W). For the wavelength λ_(k),r(i,j,λ_(k))=the number of links if one or more links exist between nodei and node j, and r(i,j,λ_(k))=0 otherwise.

Next at step 230, the link-diversity path number c(i,j,λ_(k)) betweeneach pair of border nodes is obtained from the adjacent matrix R_(λ)_(k) . The term “link-diversity path” used herein indicates a pathwithout any shared links between two border nodes.

In an embodiment, computing the link-diversity path number between twoborder nodes is turned into computing maximum flow between two bordernodes, as the link-diversity path number between two border nodes equalsto the maximum flow between a source and a destination in the canalnetwork. Thus, in this embodiment, the link-diversity path numberbetween two border nodes is computed by calculating the maximum flowbetween them by utilizing a maximum flow algorithm.c(i,j,λ _(k))=MAXFLOW(i,j,R _(λ) _(k) )   formula (4)

In an embodiment, the maximum flow between two border nodes iscalculated by utilizing the existing highest-label-preflow-pushalgorithm. In an alternative embodiment, the maximum flow between twoborder nodes is calculated using the Augmenting Path Algorithm.

Then, at step 240, the connectivity matrix C is obtained by using thelink-diversity path number c(i,j,λ_(k)) and a corresponding full-meshtopology G_(B) is constructed. Wherein:C=[c(i,j,λ _(k))]_(N×N×W)   formula (5)G _(B)=(V _(B) , E _(B))   formula (6)wherein, M=N(N−1)/2, V_(B)={v₁, v₂, . . . , v_(N)}, E_(B)={e₁, e₂, . . ., e_(M)}, e(i,j,λ_(k))=c(i,j,λ_(k))_(o)

From the above embodiment, it can be seen that the connectivity matrix Cof the present invention is different from the conventional one shown informula (1). Instead of being represented by 0 and 1, the connectivitymatrix of the present invention is represented by the link-diversitypath number between two border nodes. By constructing the connectivitymatrix C using the link-diversity path number, not only information onavailable wavelengths is provided, but also resources information on thewavelengths is provided. Thus, the more abundant information can be usedfor helping the selection of the optimal path, and the performance canbe improved. For example, when a connection is setup, it is preferred touse the wavelength which has more resource and has less possibility tobe exhausted.

In another preferred embodiment, the resulting full-mesh topology isfurther compressed to obtain a sparser topology so that the amount ofinformation to be exchanged among domains is further reduced.

FIG. 3 shows a flow chart of a topology compression process according toan embodiment of the present invention. As shown in FIG. 3, at step 310,for the full-mesh topology, a bi-directional shuffle-net construction isperformed to obtain a bi-directional shuffle-net topology in which allnodes are null.

A conventional shuffle-net (P, K) topology is a net which has N′=KP^(K)nodes (P, K=1, 2, . . . ) arranged in K columns and P^(K) rows. Amongothers, each node in each column has P direct links to nodes in the nextcolumn, and the nodes in the last column also link to the nodes in firstcolumn so that a cylinder topology is formed. That is to say, the node(l, n) (l=0, 1, . . . , P^(K)−1, n=0, 1, . . . , K−1) has P direct linksto (l′, n′), (l′, n′+1), . . . , (l′, n′+P−1) in the next column, wherel′=l mod P^(K)−1, n′=(n+1) mod K. The bi-directional shuffle-nettopology is an extended version of the shuffle-net. The bi-directionalshuffle-net topology is different from the conventional shuffle-nettopology in that its links are all bi-directional, that is to say, eachnode in each column not only has P links to nodes in the next column,but has P links to nodes in the previous column.

FIG. 4 shows an example of a bi-directional shuffle-net topology,wherein K=2, P=2, N′=8. As shown, the 8 nodes are arranged in 2 columnsand 4 rows. Each node in each column has 2 links to the next andprevious column respectively. With the bi-directional shuffle-nettopology, logical links number can be reduced to PN′ which issignificantly reduced than that of the full-mesh topology which is theorder of N², thereby reducing the amount of the link information needingto be flooded and alleviating the load of the signaling network.

In the following, a process of the bi-directional shuffle-net topologyconstruction of the present invention will be described in detail withreference to FIG. 5.

As shown in FIG. 5, first at step 510, parameters K and P of thebi-directional shuffle-net topology are computed for the full-meshtopology, wherein K and P can be solved by the following formula:Max{(K−1)P ^(K−1) , K(P−1)^(K) }<N≦KP ^(K)(K, P≧2)   formula (7)wherein N is the number of nodes in the full-mesh topology, i.e., thenumber of border nodes in the real topology.

Next, at step 520, the bi-directional shuffle-net topology isinitialized by utilizing the parameters K and P obtained from formula(7), in order to obtain an initialized bi-directional shuffle-nettopology:G′=(V′, E′)   formula (8)wherein, V′ is the set of nodes in the bi-directional shuffle-nettopology, i.e., V′={v′₁, v′₂, . . . , v′_(N′)}, and E′ is the set oflinks in the bi-directional shuffle-net topology, i.e., E′={e′₁, e′₂, .. . , e′_(M′)}, e_(l)′={λ′₁, λ′₂, . . . , λ′_(W)}, with N′=KP^(K),M′=KP^(K+1), V′

V_(B), and E′

E_(B)

Back to FIG. 3, at step 320, the nodes in the full-mesh topology aremapped to nodes in the shuffle-net by means of a certain mappingrelation, and corresponding logical link attributes are imparted to thebi-directional links in the shuffle-net.

FIG. 6 shows a flow chart of a process of optimizing a bi-directionshuffle-net topology according to an embodiment of the presentinvention. In the embodiment, the Genetic Algorithm (GA) is used foroptimizing the bi-direction shuffle-net topology.

As shown, at step 610, the first generation P0 of the bi-directionshuffle-net topology is first obtained, which includes severalchromosomes that can represent the way by which the nodes of thefull-mesh topology are arranged in the bi-direction shuffle-nettopology. In an embodiment, the one-dimensional literal permutationencoding is used to construct the chromosomes. For example, thechromosomes of the first generation P0 can be obtained by permuting N′nodes randomly:p=random permutation(1, 2, . . . , N′)p□ P ₀   formula (9)

Taking a full-mesh topology of which the number of nodes is 7 as anexample, a bi-directional shuffle-net topology with K=2, P=2 and N′=8 isobtained by the aforesaid initializing operation. For a randomlygenerated chromosome ‘7 2 3 1 5 6 4 8’ , it represents the orderaccording to which each node of the full-mesh topology is arranged inthe bi-directional shuffle-net (wherein node 8 is a virtual node that isfilled for making up the deficiency), i.e., node 7 in the full-meshtopology corresponds to the node (0, 0) in the bi-directionalshuffle-net, node 2 corresponds to the node (1, 0), . . . , node 4corresponds to the node (2, 1), and virtual node 8 corresponds to thenode (3, 1), as shown in FIG. 7.

It can be seen that the chromosomes in the present invention aredifferent from those ordinarily composed of 0-1 bit sequence. Thepresent invention utilizes the one-dimensional literal permutationencoding, wherein each gene is the node number. Therefore, mutationoperation on chromosomes of the present invention is position-based,which usually changes the position of genes in the chromosomes, thus itis ensured that the next generation can inherit most characteristicsfrom their parents and compatible genes can be reserved.

Next, at step 620, after all nodes V_(B) of the full-mesh topology G_(B)is mapped into nodes V′ of the bi-direction shuffle-net topology G′ inaccordance with each chromosome p, the mapping of logical links can beperformed according to the node positions. As in the bi-directionalshuffle-net topology mentioned above, node 7 is connected to node 4, 5,and 6, thus the bi-directional logical links (4, 7), (5, 7), (6, 7) arereserved, and other logical links connected with node 7 are all deleted.Node 8 is a virtual node and doesn't participate in the mapping oflogical links. For purpose of evaluating each chromosome, we can obtaina corresponding connectivity matrix C′ of the bi-directional shuffle-nettopology G′:C′=[c′(i,j,λ _(k))]_(N×N×W)   formula (10)

The connectivity matrix C′ can be obtained in a manner similar to obtainthe connectivity matrix C.

At step 630, a fitness value of each chromosome then is obtained withthe connectivity matrix of border nodes of the real topology as a targetfunction, which is:

$\begin{matrix}{{Fitness} = {- {\sum\limits_{\lambda = 1}^{W}{\sum\limits_{i,{j = 1}}^{N}{{devation}\left( {i,j,\lambda_{k}} \right)}}}}} & {{formula}\mspace{14mu}(11)}\end{matrix}$

wherein:

$\begin{matrix}{{{deviation}\left( {i,j,\lambda_{k}} \right)} = \left\{ \begin{matrix}{0,} & {{c^{\prime}\left( {i,j,\lambda_{k}} \right)} = {c\left( {i,j,\lambda_{k}} \right)}} \\1 & {{c^{\prime}\left( {i,j,\lambda_{k}} \right)} = {{0\mspace{14mu}{or}\mspace{14mu}{c\left( {i,j,\lambda_{k}} \right)}} = 0}} \\{\frac{{c^{\prime}\left( {i,j,\lambda_{k}} \right)} - {c\left( {i,j,\lambda_{k}} \right)}}{{c^{\prime}\left( {i,j,\lambda_{k}} \right)} + {c\left( {i,j,\lambda_{k}} \right)}}} & {others}\end{matrix} \right.} & {{formula}\mspace{14mu}(12)}\end{matrix}$

In formula (11), the fitness value represents the degree ofapproximation between the connectivity matrix of the full-mesh topologyand the connectivity matrix of the topology of the chromosome, the valueof which is the opposite of the sum of the degree of deviation. Theminus indicates that the smaller the deviation value is, the larger thefitness value is. The deviation value can be the absolute value of theratio of the difference to the sum between the connectivity matrix ofthe topology of the chromosome and the target connectivity matrix. In analternative embodiment, the deviation value is the absolute value of thedifference between the connectivity matrix of the topology of thechromosome and the target connectivity matrix.

Next, at step 640, genetic operation is performed based on the obtainedfitness value to obtain chromosomes of the next generation. In anembodiment, a selection operation is first performed on the chromosomesof the current generation based on the obtained fitness value to obtainthe chromosomes for propagating the next generation. That is to say, thechromosome with a lower fitness value (for example, a fitness valuelower than a threshold) is culled out, and only the chromosome with ahigher fitness value is selected into the next generation. Cross-overand mutation operations are performed on the selected chromosomes toobtain the chromosomes of the next generation. FIG. 8 shows an exampleof crossover and mutation operations according to an embodiment of thepresent invention. It should be appreciated that the present inventionis not limited to the crossover and mutation operations shown in FIG. 8.

Then, at step 650, it is determined whether the number of the iterationreaches a predetermined threshold or not. If the answer is yes, theresulting chromosome with the best fitness is taken as the optimizationresult at step 660, that is to say, the optimized bi-directionalshuffle-net topology is obtained. If the answer is no, the processproceeds to step 620, and continues the iteration.

In another embodiment, the genetic operations are terminated if thefitness value of a chromosome reaches a predetermined threshold beforethe predetermined threshold for the number of the iteration is reached,and the bi-directional shuffle-net topology where the nodes are arrangedand connected in accordance with the obtained chromosome, is taken asthe resulting topology.

The number of iteration can be determined based on need. The more thenumber of iteration is, the better the obtained result is, but more timeis required to compute.

The inventor has verified that the average deviation of each generationdecreases quickly in the initial phase and reaches to a stable valueafter about 20 generations. After that, the average deviation fluctuatesin a certain range. The average deviation after 30 generations issubstantially smaller than 20%, and the optimal individual deviation issubstantially about 10%. It can bee seen that the topology aggregationmethod of the present invention can achieve a more accurate topology andthus can provide more accurate and abundant information for routingdecision, and thus can improve the performance.

Furthermore, other suitable algorithms known to the skilled in the art,such as the heuristic algorithm and the like, can also be used tooptimize the bi-directional shuffle-net topology.

It can be seen from the description with reference to FIGS. 3 to 8, thatthe topology aggregation method of the present invention providesdetailed link information suitable for the optical network. Moreparticularly, by employing the genetic algorithm to optimize thebi-directional shuffle-net topology, the more accurate topologystructure is described using less logical links, and thus theperformance is improved.

In another alternative embodiment, the nodes with more link-diversitypath number in the full-mesh topology are mapped into nodes connecteddirectly in a shuffle-net topology using the obtained connectivitymatrix of the full-mesh topology, thereby compressing the full-meshtopology into a bi-directional shuffle-net topology using the heuristicapproach.

In still another alternative embodiment, the full-mesh topology iscompressed into a symmetric-star topology by utilizing the obtainedconnectivity matrix of the full-mesh topology.

When the network conditions change, for example the bandwidthavailability of links changes, a re-aggregation operation needs beperformed to update the advertised aggregation topology.

In an embodiment of the present invention, the re-aggregation can beperformed based on a predetermined time interval. In this embodiment, are-aggregation time interval is predetermined, and the re-aggregationcan be performed periodically based on the predetermined time interval,regardless of the bandwidth change conditions of links in theintra-domain.

In another embodiment of the present invention, an event-basedre-aggregation policy is adopted, that is to say, the re-aggregation isperformed only when a predetermined event occurs. Preferably, a lazyre-aggregation policy can be used, i.e., the re-aggregation is performedonly when a big topology change happens or the connection rejection ratereaches a predetermined threshold.

In an embodiment, the re-aggregation is performed when a predeterminedconnection rejection rate is reached. As an example, during apredetermined re-aggregation time interval, the re-aggregation isperformed immediately when the connection rejection rate r exceeds apredetermined threshold α, wherein α is a constant based on customer'sQoS requirement, for example is 20%.

In another example, the re-aggregation is performed when the topologychanges greatly. In this embodiment, it is assumed that the linkcapacity is B channels, and the number of available channels at time tis c_(t). When the number of available channels c_(t) at time t equals avalue in a predetermined value sequence b_(k), the re-aggregation isperformed.

In a embodiment, the predetermined value sequence b_(k) is constructedas follows: b_(k)={0, L_(a)}, i.e., the re-aggregation is performed whenthe number of available channels c_(t) become 0 or L_(a).

In another embodiment, the predetermined value sequence b_(k) has moreelements so that topology re-aggregation occurs at a lower frequencywhen there are more available channels and occurs at a higher frequencywhen there are less available channels. For example, the predeterminedvalue sequence b_(k) can be obtained by the following formula:

$\begin{matrix}{b_{k} = \left\{ \begin{matrix}0 & {k = 0} \\L_{a} & {{k = 1},2,\ldots\mspace{14mu},L_{a}} \\{L_{a} +_{a}^{k - L_{a}}} & {{k = {L_{a} + 1}},{\ldots\mspace{14mu} K}}\end{matrix} \right.} & {{formula}\mspace{14mu}(13)}\end{matrix}$

wherein K is the largest index such that b_(k)<B for L_(a)>1 and b_(k)=kfor L_(a)=1. For example, in a case of 16 channels on each logical link,for L_(a)=2, the predetermined value sequence is b_(k)={0, 2, 4, 6, 10,16}; for L_(a)=3, b_(k)={0, 3, 6, 12, 16}; and for L_(a)=4, b_(k)={0, 4,8, 16}. The foregoing formula is just an example, and other methods canbe used to obtain the predetermined value sequence b_(k).

In accordance with the re-aggregation policy of the present invention,the re-aggregation is preformed only when a big topology change occurs,and is not preformed when the topology change is small. Therefore, inthe case of ensuring that aggregation topology is updated in time, thenumber of re-aggregation is reduced, thereby reducing the consumedresources due to the implementation of re-aggregation and improving theperformance.

It can be seen from the above description that, as the present inventionconstructs the connectivity matrix by utilizing “link-diversity pathnumber”, which provides not only the availability information onavailable wavelengths but also the resource abundance information onwavelengths, thereby more detailed link information is provided for therouting decision to optimize the source allocation and thus improve thenetwork performance. In a preferred embodiment, the obtained full-meshtopology is further compressed, thereby reducing the amount ofinformation to be exchanged among domains and further improving theperformance. In a preferred embodiment, the full-mesh topology iscompressed into a bi-directional shuffle-net topology to reduce thenumber of links significantly, thereby reducing the amount of routinginformation needing to be exchanged among domains and improving theperformance. In another preferred embodiment, the re-aggregation isperformed by utilizing an event-based lazy re-aggregation policy,thereby reducing the consumed resources due to the implementation ofre-aggregation and improving the performance.

Next, an apparatus for topology aggregation of the present inventionwill be further described hereinafter.

FIG. 9 shows an apparatus 900 for topology aggregation according to anembodiment of the present invention. As shown in FIG. 9, the apparatus900 for topology aggregation includes: link-diversity path numberobtaining means 910 configured to obtain the link-diversity path numberc(i,j,λ_(k)) between each pair of border nodes in a real topology; andtopology constructing means 920 configured to obtain a connectivitymatrix C by using the link-diversity path number c(i,j,λ_(k)) andconstruct a corresponding full-mesh topology G_(B).

The link-diversity path number obtaining means 910 can operate inaccordance with the above description with respect to steps 210-230: anadjacent matrix is first computed from the real topology of a domain,and then the link-diversity path number between border nodes is obtainedby calculating the maximum flow between border nodes. Thehighest-label-preflow-push algorithm, the Augmenting Path Algorithm, andother algorithms known to the skilled in the art can be used whencomputing the link-diversity path number.

The topology constructing means 920 can operate in accordance with theabove description with respect to step 240: the connectivity matrix isconstructed using the link-diversity path number obtained by thelink-diversity path number obtaining means 910.

From the above embodiment, it can be seen that the apparatus 900 fortopology aggregation of the present invention constructs theconnectivity matrix using the link-diversity path number, which providesnot only the information on available wavelengths but also the resourceinformation on the abundance degree of wavelengths, thereby facilitatingthe route selection and improving the performance.

In a preferred embodiment, the apparatus 900 for topology aggregationfurther includes topology compressing means 930 configured to compressthe full-mesh topology, thereby further reducing the amount ofinformation to be exchanged among domains.

As shown in FIG. 10, in an embodiment, the topology compressing means930 includes: bi-directional shuffle-net topology constructing means 932configured to initialize a bi-directional shuffle-net topology usingdetermined structural parameters of the bi-directional shuffle-nettopology; and bi-directional shuffle-net topology optimizing means 934configured to optimize the bi-directional shuffle-net topology using theconnectivity matrix of the full-mesh topology.

The bi-directional shuffle-net topology optimizing means 934 can furtheroptimize the bi-directional shuffle-net topology based on the geneticalgorithm. FIG. 11 shows an embodiment of the bi-directional shuffle-nettopology optimizing means 934 based on the genetic algorithm. Thebi-directional shuffle-net topology optimizing means 934 includes: firstgeneration generating means 9342 configured to obtain a first generationof the bi-direction shuffle-net topology randomly; connectivity matrixobtaining means 9344 configured to obtain the connectivity matrix ofchromosome of the current generation; fitness value computing means 9346configured to compute the fitness value of each chromosome of thecurrent generation using the connectivity matrix of border nodes of thereal topology as a target function; genetic operation means 9348configured to perform genetic operations on the chromosomes in thecurrent generation based on the fitness value to obtain the chromosomesof the next generation, wherein the genetic operations includes aselection operation, a mutation operation, a cross-over operation, andthe like; and mapping means 9350 configured to, when a conditionterminating the genetic operations is met, map the nodes of thefull-mesh topology and the logical links into the bi-directionalshuffle-net topology by utilizing the resulting optimized chromosome.Furthermore, other suitable algorithms known to the skilled in the art,such as the heuristic algorithm and the like, can also be used tooptimize the bi-directional shuffle-net topology.

In an embodiment, the fitness value computed by the fitness valuecomputing means 9346 is the absolute of the difference between theconnectivity matrix of chromosome of said current generation and that ofborder nodes of the real topology. While in another embodiment, thefitness value is the absolute value of the ratio of the difference tothe sum between the connectivity matrix of chromosome of said currentgeneration and that of border nodes of the real topology.

In an embodiment, the condition terminating the genetic operations isthat the number of iteration exceeds a predetermined value or thefitness value reaches a predetermined value.

In an embodiment, the chromosome is based on one-dimensional literalpermutation encoding.

From the above embodiment, it can be seen that the number of the logicallinks is reduced significantly and the more accurate topology structureis provided by further optimizing the bi-directional shuffle-nettopology utilizing the genetic algorithm, thereby improving theperformance.

In a preferred embodiment, as shown in FIG. 12, the apparatus 900 fortopology aggregation further includes a re-aggregation triggering means940 configured to trigger a re-aggregation of topology when the networkstatus changes or trigger a re-aggregation of topology based on apredetermined time interval. In an embodiment, based on a lazyre-aggregation policy, the re-aggregation triggering means 940 triggersthe re-aggregation when a predetermined topology change event occurs orthe connection rejection rate reaches a predetermined threshold. Thepredetermined topology change event can be an event that the amount ofavailable resources increases or decreases to a predetermined value, andthe re-aggregation occurs at a lower frequency when there are moreavailable resources and occurs at a higher frequency when there are lessavailable resources.

Besides, the present invention also provides a routing controller. FIG.13 shows a schematic block diagram of a routing controller according toan embodiment of the present invention. As shown in FIG. 13, the routingcontroller 1300 includes: real topology obtaining means 1310 for obtaina real topology of one domain, the apparatus 900 for topologyaggregation of the present invention for aggregating the real topologyof one domain, and link-state sending means 1320 for generating theLink-State Advertisement (LSA) information based on the topologyobtained by the apparatus 900 for topology aggregation, and for sendingthe LSA information out. The apparatus 900 for topology aggregation inthe routing controller constructs the connectivity matrix using thelink-diversity path number, which provides not only information onavailable wavelengths, but also resource information on wavelengths,thereby facilitating route selection and improving the performance.

FIG. 14 shows a flowchart of a starting phase process of a routingcontroller according to an embodiment of the present invention. As shownin FIG. 14, after the routing controller starting to operate, the realtopology obtaining means first obtains a real topology of one domain atstep 1410. At step 1420, the apparatus for topology aggregation thenaggregates the real topology. Next at step 1430, the link-state sendingmeans generates LSA information based on the aggregated topology andsends it out.

In the working phase, the apparatus for topology aggregation willtrigger a re-aggregation when a re-aggregation condition is met. There-aggregation can be triggered when the network status changes ortriggered based on a predetermined time interval. In an embodiment,based on a lazy re-aggregation policy, the re-aggregation is triggeredwhen a predetermined topology change event occurs or the connectionrejection rate reaches a predetermined threshold. Besides, thepredetermined topology change event can be an event that the amount ofavailable resources increases or decreases to a predetermined value, andthe predetermined event occurs at a lower frequency when there are moreavailable resources and occurs at a higher frequency when there are lessavailable resources.

FIG. 15 shows a flowchart of a working phase process of a routingcontroller according to an embodiment of the present invention.

As shown in FIG. 15, at the working phase, when a request message isreceived, it is first determined that the request is a connection setuprequest or a connection release request at step 1501. If it is aconnection setup request, the process proceeds to step 1502.

At step 1502, the resources between the designated entrance and exitnodes are checked, and if the check shows that there are no enoughresources between the designated entrance and exit nodes to support theconnection request, the process proceeds to step 1504. At step 1504, amessage is sent to the requestor to notify the requestor the failure ofthe request. At step 1505, the failure number is then increased by 1,and the rejection rate of connections, i.e., the failure number/(thefailure number+the success number) is computed.

Next, at step 1506, it is determined whether the rejection rate reachesa predetermined threshold of the rejection rate. If the predeterminedthreshold is reached, then the re-aggregation is triggered at step 1513to perform the topology re-aggregation. After this, at step 1514, thecounts of the success and failure of connections are reset to countagain. Then at step 1515, LSA is generated based on the re-aggregatedtopology, and sent out. Thus a process for a request is over.

On the other hand, if at step 1502 the check shows that there are enoughresources between the designated entrance and exit nodes to support theconnection request, the process proceeds to step 1507. At step 1507, therequest is sent to a next domain, and a connection setup indication iswaited. After receiving the indication, at step 1508, the wavelength isallocated along the intra selected links to setup a connection. Afterthis, at step 1509, the success number of connections is increased by 1.Then at step 1510, a re-flooding is triggered to send LSA information.Next at step 1511, the residual resources between the entrance and exitnodes are checked, and at step 1512, it is determined whether the amountof residual resources reaches a predetermined threshold ofre-aggregation L_(a) or not. If the threshold L_(a) is reached, theprocess enters into step 1513 and performs the re-aggregation. Afterthis, at step 1514 the counts of the success and failure connections arereset. The process then proceeds to step 1515 and the process for therequest is terminated. If the threshold L_(a) is not reached yet, thenthe process for the request is terminated directly.

In step 1501, if the request is a connection release request, theprocess proceeds to step 1503. At this step, the connection is released.The process goes to step 1510 subsequently, the steps as described aboveis performed until the process is terminated.

It will be noted that more specific technical details known to theskilled to the art, which may be necessary to the implementation of thepresent invention, are omitted in above description for much betterunderstanding of the present invention.

The specification is provided for the purpose of illustration anddescription, but is not exhaustive or limited the invention to thedisclosed form. Various modifications and alterations are apparent tothe skilled in the art.

Therefore, the embodiments were chosen and described in order to bestexplain the principles of the invention, the practical applicationthereof, and to enable the skilled in the art to understand that allmodifications and alterations made without departing from the spirit ofthe present invention fall into the protection scope of the presentinvention as defined in the appended claims.

What is claimed is:
 1. A method for topology aggregation by a routingcontroller in an optical network, comprising the steps of: obtaining anadjacent matrix for each wavelength in used for communication within areal topology of nodes in a routing domain, communication between saidnodes managed by the routing controller; obtaining a link-diversity pathnumber between each pair of border nodes in the real topology from theadjacent matrices using a maximum flow algorithm; obtaining aconnectivity matrix by utilizing said link-diversity path numbers andconstructing a corresponding full-mesh topology; and performing topologycompression on said full-mesh topology, said topology compressionfurther comprising the steps of: determining structural parameters of abi-directional shuffle-net topology; initializing said bi-directionalshuffle-net topology by utilizing said structural parameters; andoptimizing said bi-directional shuffle-net topology by utilizing saidconnectivity matrix.
 2. The method of claim 1, wherein saidlink-diversity path number is obtained by the step of calculating amaximum flow between border nodes.
 3. The method of claim 2, wherein thestep of calculating the maximum flow between border nodes is performedby the step of utilizing the highest-label-preflow-push algorithm. 4.The method of claim 1, wherein the step of optimizing saidbi-directional shuffle-net topology is based on a genetic algorithm, andfurther comprises the steps of: obtaining a first generation of saidbi-directional shuffle-net topology randomly; obtaining a connectivitymatrix of a topology of a chromosome of a current generation; computinga fitness value for each chromosome of the current generation using theconnectivity matrix of border nodes in the real topology as a targetfunction; performing genetic operations on each chromosome of thecurrent generation based on said fitness value to obtain chromosomes ofthe next generation; repeating the steps of obtaining the connectivitymatrix of the topology of the chromosome, computing the fitness value,and performing the genetic operations until a condition terminating thegenetic operations is met; and mapping nodes of the full-mesh topologyinto the bi-directional shuffle-net topology by utilizing a resultingchromosome to obtain an optimized bi-directional shuffle-net topology.5. The method of claim 4, wherein said fitness value is an opposite of asum of each deviation value, said each deviation value being an absolutevalue of a ratio of a difference to the sum between the connectivitymatrix of the topology of the chromosome and the connectivity matrix ofsaid full-mesh topology.
 6. The method of claim 4, further comprisingthe step of terminating the genetic operations when a number of aniteration reaches a predetermined value.
 7. The method of claim 4,wherein said chromosome utilizes one-dimensional literal permutationencoding.
 8. The method of claim 1, further comprising the step ofperforming a re-aggregation of topology when the network status changes.9. The method of claim 8, wherein the re-aggregation of topology isbased on a lazy re-aggregation policy and is performed when apredetermined topology change event occurs or the connection rejectionrate reaches a predetermined threshold.
 10. The method of claim 9,wherein said predetermined topology change event is an event that anamount of available resources becomes a predetermined value, and saidpredetermined topology change event occurs at a lower frequency whenthere are more available resources and occurs at a higher frequency whenthere are less available resources.
 11. An apparatus for topologyaggregation of nodes in an optical network, comprising: a link-diversitypath number obtaining means configured to obtain an adjacent matrix foreach wavelength in a real topology and obtain a link-diversity pathnumber between each pair of border nodes in the real topology from theadjacent matrices using a maximum flow algorithm; a topologyconstructing means configured to obtain a connectivity matrix by usingsaid link-diversity path numbers and construct a corresponding full-meshtopology; and a topology compression means configured to performtopology compression on said full-mesh topology, said topologycompression performing steps comprising: determining structuralparameters of a bi-directional shuffle-net topology; initializing saidbi-directional shuffle-net topology by utilizing said structuralparameters; and optimizing said bi-directional shuffle-net topology byutilizing said connectivity matrix.
 12. A routing controller,comprising: an apparatus for topology aggregation comprising: alink-diversity path number obtaining means configured to obtain anadjacent matrix for each wavelength in a real topology and obtain alink-diversity path number between each pair of border nodes in the realtopology from the adjacent matrices using a maximum flow algorithm; atopology constructing means configured to obtain a connectivity matrixby using said link-diversity path numbers and construct a correspondingfull-mesh topology; and a topology compression means configured toperform topology compression on said full-mesh topology, said topologycompression means performing steps comprising: determining structuralparameters of a bi-directional shuffle-net topology; initializing saidbi-directional shuffle-net topology by utilizing said structuralparameters; and optimizing said bi-directional shuffle-net topology byutilizing said connectivity matrix; and a link-state sending meansconfigured to a) generate Link-State Advertisement (LSA) informationbased on the topology obtained by the apparatus for topology aggregationand b) send said LSA information out.